MaxGekk commented on a change in pull request #32295:
URL: https://github.com/apache/spark/pull/32295#discussion_r618292644
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File path: docs/sql-migration-guide.md
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@@ -79,9 +79,13 @@ license: |
- In Spark 3.2, `TRANSFORM` operator can't support alias in inputs. In Spark
3.1 and earlier, we can write script transform like `SELECT TRANSFORM(a AS c1,
b AS c2) USING 'cat' FROM TBL`.
+<<<<<<< HEAD
- In Spark 3.2, `TRANSFORM` operator can support
`ArrayType/MapType/StructType` without Hive SerDe, in this mode, we use
`StructsToJosn` to convert `ArrayType/MapType/StructType` column to `STRING`
and use `JsonToStructs` to parse `STRING` to `ArrayType/MapType/StructType`. In
Spark 3.1, Spark just support case `ArrayType/MapType/StructType` column as
`STRING` but can't support parse `STRING` to `ArrayType/MapType/StructType`
output columns.
- In Spark 3.2, the unit-to-unit interval literals like `INTERVAL '1-1' YEAR
TO MONTH` are converted to ANSI interval types: `YearMonthIntervalType` or
`DayTimeIntervalType`. In Spark 3.1 and earlier, such interval literals are
converted to `CalendarIntervalType`. To restore the behavior before Spark 3.2,
you can set `spark.sql.legacy.interval.enabled` to `true`.
+=======
+ - In Spark 3.2, `String_Column - Date_Column` will cause an analysis
exception instead of implicitly converting the first column as Date Type. This
is to make it consistent with the behavior of `String_Column -
Timestamp_Column`.
Review comment:
BTW, is `String_Column` common notion? Why not just `string column` or
`<string column>` like in SQL standard.
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